Jupyter Notebook is available at : Single Docking¶

Single Docking Workflow with DockSuiteX¶

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import sys 
import os
sys.path.insert(0, os.path.abspath('..')) 

from docksuitex import Protein, Ligand, GridCalculator, VinaDocking, AD4Docking
from docksuitex.utils import fetch_pdb, fetch_sdf, parse_vina_log, parse_ad4_dlg, view_molecule

import sys import os sys.path.insert(0, os.path.abspath('..')) from docksuitex import Protein, Ligand, GridCalculator, VinaDocking, AD4Docking from docksuitex.utils import fetch_pdb, fetch_sdf, parse_vina_log, parse_ad4_dlg, view_molecule

Fetch and prepare Protein¶

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# Fetch the protein structure
protein_path = fetch_pdb(pdbid = "1HVR")

# Visualize the downloaded protein structure
view_molecule(protein_path)

# Fetch the protein structure protein_path = fetch_pdb(pdbid = "1HVR") # Visualize the downloaded protein structure view_molecule(protein_path)

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# Prepare the protein
# This cleans the PDB, removes water/heterogens, and adds hydrogens/charges

prot = Protein(
    input = protein_path, 

    #Default parameters
    fix_pdb=True, 
    remove_heterogens=True, 
    remove_water=True,
    add_hydrogens=True, 
    add_charges=True, 
    preserve_charge_types=None, #eg: ["Zn", "Fe"]
    )

prepared_prot_path = prot.prepare()

# Visualize the prepared protein structure
prot.view_molecule()

# Prepare the protein # This cleans the PDB, removes water/heterogens, and adds hydrogens/charges prot = Protein( input = protein_path, #Default parameters fix_pdb=True, remove_heterogens=True, remove_water=True, add_hydrogens=True, add_charges=True, preserve_charge_types=None, #eg: ["Zn", "Fe"] ) prepared_prot_path = prot.prepare() # Visualize the prepared protein structure prot.view_molecule()

Find Binding Pockets¶

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# Predict binding pockets using P2Rank or define a blind docking box
# Identify binding pockets on the protein surface using P2Rank
gc = GridCalculator(receptor=prepared_prot_path, mode="p2rank") # mode can be "p2rank" or "blind"
pockets = gc.run()

# Predict binding pockets using P2Rank or define a blind docking box # Identify binding pockets on the protein surface using P2Rank gc = GridCalculator(receptor=prepared_prot_path, mode="p2rank") # mode can be "p2rank" or "blind" pockets = gc.run()

Fetch and prepare Ligand¶

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# Fetch the ligand structure
ligand_path = fetch_sdf(molecule_id="2244")

# Visualize the downloaded ligand structure
view_molecule(ligand_path)

# Fetch the ligand structure ligand_path = fetch_sdf(molecule_id="2244") # Visualize the downloaded ligand structure view_molecule(ligand_path)

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# Prepare the ligand
# This minimizes the structure, removes water and adds hydrogens/charges

lig = Ligand(
    input=ligand_path, 

    #Default parameters
    minimize=None,  #Options: "mmff94", "mmff94s", "uff", "gaff"
    remove_water=True, 
    add_hydrogens=True, 
    add_charges=True, 
    preserve_charge_types=None #eg: ["Zn", "Fe"]
)

prepared_lig_path = lig.prepare()

# Visualize the prepared ligand structure
lig.view_molecule()

# Prepare the ligand # This minimizes the structure, removes water and adds hydrogens/charges lig = Ligand( input=ligand_path, #Default parameters minimize=None, #Options: "mmff94", "mmff94s", "uff", "gaff" remove_water=True, add_hydrogens=True, add_charges=True, preserve_charge_types=None #eg: ["Zn", "Fe"] ) prepared_lig_path = lig.prepare() # Visualize the prepared ligand structure lig.view_molecule()

Perform protein ligand docking at the first pocket¶

Using AutoDock Vina¶

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# Configure the docking engine with receptor, ligand, and grid parameters
vina = VinaDocking(
    receptor=prepared_prot_path,
    ligand=prepared_lig_path,
    grid_center=pockets[0]['center'],   #First pocket center 
    grid_size=pockets[0]['grid_size'],     #Grid Size of first pocket center
    
    # Default Parameters
    exhaustiveness=8,
    num_modes=9,
    verbosity=1,
    seed=42, # Random seed for reproducibility
)

# Configure the docking engine with receptor, ligand, and grid parameters vina = VinaDocking( receptor=prepared_prot_path, ligand=prepared_lig_path, grid_center=pockets[0]['center'], #First pocket center grid_size=pockets[0]['grid_size'], #Grid Size of first pocket center # Default Parameters exhaustiveness=8, num_modes=9, verbosity=1, seed=42, # Random seed for reproducibility )

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# Visualize grid box for Vina
vina.view_grid_box()

# Visualize grid box for Vina vina.view_grid_box()

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# Run docking and save results
vina.run(save_to=f"vina_results")

vina.parse_results()

# Run docking and save results vina.run(save_to=f"vina_results") vina.parse_results()

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#Interactively view the docked poses
vina.view_docked_poses()

#Interactively view the docked poses vina.view_docked_poses()

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# Compute interaction fingerprints and identify types of contacts
vina.interaction_profile()

# Compute interaction fingerprints and identify types of contacts vina.interaction_profile()

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vina.view_interactions()

vina.view_interactions()

Using AutoDock4¶

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# Configure the docking engine with receptor, ligand, and grid parameters
ad4 = AD4Docking(
    receptor=prepared_prot_path,
    ligand=prepared_lig_path,
    grid_center=pockets[0]['center'],   #First pocket center
    grid_size=pockets[0]['grid_size'],    #Grid Size of first pocket center

    #Default Parameters
    spacing=0.375,     
    dielectric=-0.1465, 
    smooth=0.5,          
    ga_pop_size=150,
    ga_num_evals=2500000,
    ga_num_generations=27000,
    ga_elitism=1,
    ga_mutation_rate=0.02,
    ga_crossover_rate=0.8,
    ga_run=10,
    rmstol=2.0,  
    seed=(27,6), # Random seed for reproducibility
)

# Configure the docking engine with receptor, ligand, and grid parameters ad4 = AD4Docking( receptor=prepared_prot_path, ligand=prepared_lig_path, grid_center=pockets[0]['center'], #First pocket center grid_size=pockets[0]['grid_size'], #Grid Size of first pocket center #Default Parameters spacing=0.375, dielectric=-0.1465, smooth=0.5, ga_pop_size=150, ga_num_evals=2500000, ga_num_generations=27000, ga_elitism=1, ga_mutation_rate=0.02, ga_crossover_rate=0.8, ga_run=10, rmstol=2.0, seed=(27,6), # Random seed for reproducibility )

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# Visualize grid box for AD4
ad4.view_grid_box()

# Visualize grid box for AD4 ad4.view_grid_box()

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# Run docking and save results
ad4.run(save_to="ad4_results")    

ad4.parse_results()

# Run docking and save results ad4.run(save_to="ad4_results") ad4.parse_results()

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#Interactively view the docked poses
ad4.view_docked_poses()

#Interactively view the docked poses ad4.view_docked_poses()

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# Compute interaction fingerprints and identify types of contacts
ad4.interaction_profile()

# Compute interaction fingerprints and identify types of contacts ad4.interaction_profile()

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ad4.view_interactions()

ad4.view_interactions()